Introduction This dataset supports Ye et al. 2024 Nature Communications. Ye, S., Filippova, A., Lauer, J. et al. SuperAnimal pretrained pose estimation models for behavioral analysis. Nat Commun 15, 5165 (2024). https://doi.org/10.1038/s41467-024-48792-2 Please cite this dataset and paper if you use this resource. Please also see Ye et al. 2024 for the full DataSheet accompanying this download, including the metadata for how to use this data if you want to compare model results on benchmark tasks. Below is just a summary. Also see the dataset licensing below. # Training Data It consists of being trained together on the following datasets: AwA-Pose Quadruped dataset, see full details at (1). AnimalPose See full details at (2). AcinoSet See full details at (3). Horse-30 Horse-30 dataset, benchmark task is called Horse-10; See full details at (4). StanfordDogs See full details at (5, 6). AP-10K See full details at (7). iRodent We utilized the iNaturalist API functions for scraping observations with the taxon ID of Suborder Myomorpha (8). The functions allowed us to filter the large amount of observations down to the ones with photos under the CC BY-NC creative license. The most common types of rodents from the collected observations are Muskrat (Ondatra zibethicus), Brown Rat (Rattus norvegicus), House Mouse (Mus musculus), Black Rat (Rattus rattus), Hispid Cotton Rat (Sigmodon hispidus), Meadow Vole (Microtus pennsylvanicus), Bank Vole (Clethrionomys glareolus), Deer Mouse (Peromyscus maniculatus), White-footed Mouse (Peromyscus leucopus), Striped Field Mouse (Apodemus agrarius). We then generated segmentation masks over target animals in the data by processing the media through an algorithm we designed that uses a Mask Region Based Convolutional Neural Networks(Mask R-CNN) (9) model with a ResNet-50-FPN backbone (10), pretrained on the COCO datasets (11). The processed 443 images were then manually labeled with both pose annotations and segmentation masks. iRodent data is banked at https://zenodo.org/record/8250392. APT-36K See full details at (12). Here is an image with a keypoint guide. #Ethical Considerations • No experimental data was collected for this model; all datasets used are cited above. #Caveats and Recommendations • Please note that each dataest was labeled by separate labs & separate individuals, therefore while we map names to a unified pose vocabulary, there will be annotator bias in keypoint placement (See Ye et al. 2024 for our Supplementary Note on annotator bias). You will also note the dataset is highly diverse across species, but collectively has more representation of domesticated animals like dogs, cats, horses, and cattle. We recommend if performance of a model trained on this data is not as good as you need it to be, first try video adaptation (see Ye et al. 2024), or fine-tune the weights with your own labeling. #License Modified MIT. Copyright 2023-present by Mackenzie Mathis, Shaokai Ye, and contributors. Permission is hereby granted to you (hereafter "LICENSEE") a fully-paid, non-exclusive, and non-transferable license for academic, non-commercial purposes only (hereafter “LICENSE”) to use the "DATASET" subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software: This data or resulting software may not be used to harm any animal deliberately. LICENSEE acknowledges that the DATASET is a research tool. THE DATASET IS PROVIDED “AS IS”, WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE DATASET OR THE USE OR OTHER DEALINGS IN THE DATASET. If this license is not appropriate for your application, please contact Prof. Mackenzie W. Mathis (mackenzie@post.harvard.edu) for a commercial use license. Please cite Ye et al 2024 if you use this DATASET in your work. #References Prianka Banik, Lin Li, and Xishuang Dong. A novel dataset for keypoint detection of quadruped animals from images. ArXiv, abs/2108.13958, 2021 Jinkun Cao, Hongyang Tang, Haoshu Fang, Xiaoyong Shen, Cewu Lu, and Yu-Wing Tai. Cross-domain adaptation for animal pose estimation. 2019 IEEE/CVF International Conference on Computer Vision (ICCV), pages 9497–9506, 2019. Daniel Joska, Liam Clark, Naoya Muramatsu, Ricardo Jericevich, Fred Nicolls, Alexander Mathis, Mackenzie W. Mathis, and Amir Patel. Acinoset: A 3d pose estimation dataset and baseline models for cheetahs in the wild. 2021 IEEE International Conference on Robotics and Automation (ICRA), pages 13901–13908, 2021. Alexander Mathis, Thomas Biasi, Steffen Schneider, Mert Yuksekgonul, Byron Rogers, Matthias Bethge, and Mackenzie W Mathis. Pretraining boosts out-of-domain robustness for pose estimation. In Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pages 1859–1868, 2021. Aditya Khosla, Nityananda Jayadevaprakash, Bangpeng Yao, and Li Fei-Fei. Novel dataset for fine-grained image categorization. In First Workshop on Fine-Grained Visual Categorization, IEEE Conference on Computer Vision and Pattern Recognition, Colorado Springs, CO, June 2011. Benjamin Biggs, Thomas Roddick, Andrew Fitzgibbon, and Roberto Cipolla. Creatures great and smal: Recovering the shape and motion of animals from video. In Asian Conference on Computer Vision, pages 3–19. Springer, 2018. Hang Yu, Yufei Xu, Jing Zhang, Wei Zhao, Ziyu Guan, and Dacheng Tao. Ap-10k: A benchmark for animal pose estimation in the wild. In Thirty-fifth Conference on Neural Information Processing Systems Datasets and Benchmarks Track (Round 2), 2021. iNaturalist. OGBIF Occurrence Download. https://doi.org/10.15468/dl.p7nbxt. iNaturalist, July 2020 Kaiming He, Georgia Gkioxari, Piotr Dollár, and Ross Girshick. Mask r-cnn. In Proceedings of the IEEE international conference on computer vision, pages 2961–2969, 2017. Tsung-Yi Lin, Piotr Dollár, Ross Girshick, Kaiming He, Bharath Hariharan, and Serge Belongie. Feature pyramid networks for object detection, 2016. Tsung-Yi Lin, Michael Maire, Serge J. Belongie, Lubomir D. Bourdev, Ross B. Girshick, James Hays, Pietro Perona, Deva Ramanan, Piotr Doll’ar, and C. Lawrence Zitnick. Microsoft COCO: common objects in context. CoRR, abs/1405.0312, 2014 Yuxiang Yang, Junjie Yang, Yufei Xu, Jing Zhang, Long Lan, and Dacheng Tao. Apt-36k: A large-scale benchmark for animal pose estimation and tracking. Advances in Neural Information Processing Systems, 35:17301–17313, 2022

Three wild-type (C57BL/6J) male mice ran on a paper spool following odor trails (Mathis et al 2018). These experiments were carried out in the laboratory of Venkatesh N. Murthy at Harvard University. Data were recorded at 30 Hz with 640 x 480 pixels resolution acquired with a Point Grey Firefly FMVU-03MTM-CS. One human annotator was instructed to localize the 12 keypoints (snout, left ear, right ear, shoulder, four spine points, tail base and three tail points). All surgical and experimental procedures for mice were in accordance with the National Institutes of Health Guide for the Care and Use of Laboratory Animals and approved by the Harvard Institutional Animal Care and Use Committee. 161 frames were labeled, making this a real-world sized laboratory dataset.

This dataset presents a vision and perception research dataset collected in Rome, featuring RGB data, 3D point clouds, IMU, and GPS data. We introduce a new benchmark targeting visual odometry and SLAM, to advance the research in autonomous robotics and computer vision. This work complements existing datasets by simultaneously addressing several issues, such as environment diversity, motion patterns, and sensor frequency. It uses up-to-date devices and presents effective procedures to accurately calibrate the intrinsic and extrinsic of the sensors while addressing temporal synchronization. During recording, we cover multi-floor buildings, gardens, urban and highway scenarios. Combining handheld and car-based data collections, our setup can simulate any robot (quadrupeds, quadrotors, autonomous vehicles). The dataset includes an accurate 6-dof ground truth based on a novel methodology that refines the RTK-GPS estimate with LiDAR point clouds through Bundle Adjustment. All sequences divided in training and testing are accessible through our website.

The ARKitFace dataset is established by this work in order to train and evaluate both 3D face shape and 6DoF in the setting of perspective projection. A total of 500 volunteers, aged 9 to 60, are invited to record the dataset. They sit in a random environment, and the 3D acquisition equipment is fixed in front of them, with a distance ranging from about 0.3m to 0.9m. Each subject is asked to perform 33 specific expressions with two head movements (from looking left to looking right / from looking up to looking down). 3D acquisition equipment we used is an iPhone 11. The shape and location of human face are tracked by structured light sensor. The triangle mesh and 6DoF information of the RGB images are obtained by built-in ARKit toolbox. The triangle mesh is made up of 1,220 vertices and 2,304 triangles. In total, 902,724 2D facial images (resolution 1280×720 or 1440×1280) with ground-truth 3D mesh and 6DoF pose annotation are collected.

BASEPROD provides comprehensive rover sensor data collected over a 1.7 km traverse, accompanied by high-resolution 2D and 3D drone maps of the terrain. The dataset also includes laser-induced breakdown spectroscopy (LIBS) measurements from key sampling sites along the rover's path, as well as weather station data to contextualize environmental conditions. The rover is a half-scale model of the ExoMars rover, featuring a sophisticated six-wheel triple-bogie suspension system. Each wheel assembly includes three actuators— one for wheel deployment (see e.g., wheel-walking), one for steering, and one for driving — and is equipped with a force-torque sensor. The sensor suite also comprises an RGB-D camera, a thermal camera, and a stereo camera mounted on a mast with pan-tilt capability. Additionally, the dataset contains IMU, GNSS, and gyroscope measurements, facilitating comprehensive analysis. Potential Applications: Research in navigation, localization, mapping, odometry, and terrain classification or segmentation tasks. It provides a rich resource for developing and testing algorithms in planetary exploration conditions.

BlendMimic3D is a pioneering synthetic dataset developed using Blender, designed to enhance Human Pose Estimation (HPE) research. This dataset features diverse scenarios including self-occlusions, object-based occlusions, and out-of-frame occlusions, tailored for the development and testing of advanced HPE models. Main features: * Realistic Environments: BlendMimic3D encompasses simple environments, resembling Human3.6M dataset, shopping activities and multi-person contexts, simulating real-world environments. * Diverse Occlusion Scenarios: Specifically addresses self-occlusions, object-based occlusions, and out-of-frame occlusions. * Multi-Perspective Capture: Utilizes four cameras to capture diverse human movements and interactions from multiple angles. * Pixel-Perfect Annotations: Offers detailed annotations for 2D keypoints, 3D keypoints, and occlusion data.

ContactArt is a dataset for learning hand-object interaction priors for hand and articulated object pose estimation. The dataset is created using visual teleoperation, where the human operator can directly play within a physical simulator to manipulate the articulated objects. All the object models are from Partnet dataset for the convenience of scaling up. ContactArt can provide accurate annotation, rich hand-object interaction, and contact information. Source: ContactArt: Learning 3D Interaction Priors for Category-level Articulated Object and Hand Poses Estimation Image Source: ContactArt: Learning 3D Interaction Priors for Category-level Articulated Object and Hand Poses Estimation

DHP19 is the first human pose dataset with data collected from DVS event cameras. It has recordings from 4 synchronized 346x260 pixel DVS cameras and marker positions in 3D space from Vicon motion capture system. The files have event streams and 3D positions recorded from 17 subjects each performing 33 movements. Source: DHP19

DRACO20K dataset is used for evaluating object canonicalization on methods that estimate a canonical frame from a monocular input image. Provides: 1. Mixed Reality Multi-view RGB-D images rendered from ShapeNet objects 2. Camera poses 3. NOCS maps 4. Semantic 2D keypoints with visibility 5. Object-centric mask

Estimating camera motion in deformable scenes poses a complex and open research challenge. Most existing non-rigid structure from motion techniques assume to observe also static scene parts besides deforming scene parts in order to establish an anchoring reference. However, this assumption does not hold true in certain relevant application cases such as endoscopies. To tackle this issue with a common benchmark, we introduce the Drunkard’s Dataset, a challenging collection of synthetic data targeting visual navigation and reconstruction in deformable environments. This dataset is the first large set of exploratory camera trajectories with ground truth inside 3D scenes where every surface exhibits non-rigid deformations over time. Simulations in realistic 3D buildings lets us obtain a vast amount of data and ground truth labels, including camera poses, RGB images and depth, optical flow and normal maps at high resolution and quality.

Event-Human3.6m is a challenging dataset for event-based human pose estimation by simulating events from the RGB Human3.6m dataset. It is built by converting the RGB recordings of Human3.6m into events and synchronising raw joints ground-truth with events frames through interpolation.

https://syncandshare.lrz.de/getlink/fi9EZb33KiSAJ5rLHAkhg7/ffhnet-data.zip

Schools of inland silversides (Menidia beryllina, n=14 individuals per school) were recorded in the Lauder Lab at Harvard University while swimming at 15 speeds (0.5 to 8 BL/s, body length, at 0.5 BL/s intervals) in a flow tank with a total working section of 28 x 28 x 40 cm as described in previous work, at a constant temperature (18±1°C) and salinity (33 ppt), at a Reynolds number of approximately 10,000 (based on BL). Dorsal views of steady swimming across these speeds were recorded by high-speed video cameras (FASTCAM Mini AX50, Photron USA, San Diego, CA, USA) at 60-125 frames per second (feeding videos at 60 fps, swimming alone 125 fps). The dorsal view was recorded above the swim tunnel and a floating Plexiglas panel at the water surface prevented surface ripples from interfering with dorsal view videos. Five keypoints were labeled (tip, gill, peduncle, dorsal fin tip, caudal tip). 100 frames were labeled, making this a real-world sized laboratory dataset.

A Simulated Benchmark for multi-modal SLAM Systems Evaluation in Large-scale Dynamic Environments.

Understanding and analyzing animal behavior is increasingly essential to protect endangered animal species. However, the application of advanced computer vision techniques in this regard is minimal, which boils down to lacking large and diverse datasets for training deep models. To break the deadlock, we present LoTE-Animal, a large-scale endangered animal dataset collected over 12 years, to foster the application of deep learning in rare species conservation. The collected data contains vast variations such as ecological seasons, weather conditions, periods, viewpoints, and habitat scenes. So far, we retrieved at least 500K videos and 1.2 million images. Specifically, we selected and annotated 11 endangered animals for behavior understanding, including 10K video sequences for the action recognition task, 28K images for object detection, instance segmentation, and pose estimation tasks. In addition, we gathered 7K web images of the same species as source domain data for the domain adaptation task. We provide evaluation results of representative vision understanding approaches and cross-domain experiments. LoTE-Animal dataset would facilitate the community to research more advanced machine learning models and explore new tasks to aid endangered animal conservation. Our dataset will be released with the paper.

All animal procedures are overseen by veterinary staff of the MIT and Broad Institute Department of Comparative Medicine, in compliance with the NIH guide for the care and use of laboratory animals and approved by the MIT and Broad Institute animal care and use committees. Video of common marmosets (Callithrix jacchus) was collected in the laboratory of Guoping Feng at MIT. Marmosets were recorded using Kinect V2 cameras (Microsoft) with a resolution of 1080p and frame rate of 30 Hz. After acquisition, images to be used for training the network were manually cropped to 1000 x 1000 pixels or smaller. The dataset is 7,600 labeled frames from 40 different marmosets collected from 3 different colonies (in different facilities). Each cage contains a pair of marmosets, where one marmoset had light blue dye applied to its tufts. One human annotator labeled the 15 marker points on each animal present in the frame (frames contained either 1 or 2 animals). https://benchmark.deeplabcut.org/datasets.html

Dataset page: https://github.com/mosamdabhi/MBW-Data MBW - Zoo is a challenging dataset consisting image frames of tail-end distribution categories (such as Fish, Colobus Monkeys, Chimpanzees, etc.) with their corresponding 2D, 3D, and Bounding-Box labels generated from minimal human intervention. Some of the prominent use cases of this dataset include not only sparse 2D and 3D landmark prediction. The data was collected by two smartphone cameras without any constraints: meaning no guidance or instructions were given as to how the data should be collected. The intention was to mimic the data captured casually by anyone holding a smartphone grade camera. Due to this reason, the cameras were continuously moving in space changing their extrinsics with respect to each other, capturing an in-the-wild dynamic scene. This dataset could be used to benchmark robust algorithms in various computer vision tasks.

MOTFront provides photo-realistic RGB-D images with their corresponding instance segmentation masks, class labels, 2D & 3D bounding boxes, 3D geometry, 3D poses and camera parameters. The MOTFront dataset comprises 2,381 unique indoor sequences with a total of 60,000 images and is based on the 3D-FRONT dataset.

MPHOI-72 is a multi-person human-object interaction dataset that can be used for a wide variety of HOI/activity recognition and pose estimation/object tracking tasks. The dataset is challenging due to many body occlusions among the humans and objects. It consists of 72 videos captured from 3 different angles at 30 fps, with totally 26,383 frames and an average length of 12 seconds. It involves 5 humans performing in pairs, 6 object types, 3 activities and 13 sub-activities. The dataset includes color video, depth video, human skeletons, human and object bounding boxes.

MuViHand is a dataset for 3D Hand Pose Estimation that consists of multi-view videos of the hand along with ground-truth 3D pose labels. The dataset includes more than 402,000 synthetic hand images available in 4,560 videos. The videos have been simultaneously captured from six different angles with complex backgrounds and random levels of dynamic lighting. The data has been captured from 10 distinct animated subjects using 12 cameras in a semi-circle topology.

Accurately tracking the six degree-of-freedom pose of an object in real scenes is an important task in computer vision and augmented reality with numerous applications. Although a variety of algorithms for this task have been proposed, it remains difficult to evaluate existing methods in the literature as oftentimes different sequences are used and no large benchmark datasets close to real-world scenarios are available. In this paper, we present a large object pose tracking benchmark dataset consisting of RGB-D video sequences of 2D and 3D targets with ground-truth information. The videos are recorded under various lighting conditions, different motion patterns and speeds with the help of a programmable robotic arm. We present extensive quantitative evaluation results of the state-of-the-art methods on this benchmark dataset and discuss the potential research directions in this field.

The data includes all movement trajectories extracted from the videos of Parkinson's assessments using Convolutional Pose Machines (CPM) as well as the confidence values from CPM. The dataset also includes ground truth ratings of parkinsonism and dyskinesia severity using the UDysRS, UPDRS, and CAPSIT. Source: https://github.com/limi44/Parkinson-s-Pose-Estimation-Dataset

The Poser dataset is a dataset for pose estimation which consists of 1927 training and 418 test images. These images are synthetically generated and tuned to unimodal predictions. The images were generated using the Poser software package. Source: Overlapping Cover Local Regression Machines Image Source: https://www.researchgate.net/figure/Test-data-used-in-the-user-study-Left-the-pose-pictures-shown-to-the-user-Middle-the_fig17_221847487

Rendered Handpose Dataset contains 41258 training and 2728 testing samples. Each sample provides: - RGB image (320x320 pixels) - Depth map (320x320 pixels) - Segmentation masks (320x320 pixels) for the classes: background, person, three classes for each finger and one for each palm - 21 Keypoints for each hand with their uv coordinates in the image frame, xyz coordinates in the world frame and a visibility indicator - Intrinsic Camera Matrix K Source: Learning to Estimate 3D Hand Pose from Single RGB Images Image Source: https://lmb.informatik.uni-freiburg.de/resources/datasets/RenderedHandposeDataset.en.html

The Retinal Microsurgery dataset is a dataset for surgical instrument tracking. It consists of 18 in-vivo sequences, each with 200 frames of resolution 1920 × 1080 pixels. The dataset is further classified into four instrument-dependent subsets. The annotated tool joints are n=3 and semantic classes c=2 (tool and background). Source: Concurrent Segmentation and Localization for Tracking of Surgical Instruments Image Source: https://sites.google.com/site/sznitr/research/retinalmicrosurgery

SEPE 8K dataset is made of 40 different 8K (8192 x 4320) video sequences and 40 variant 8K (8192 x 5464) images. The video sequences were captured at a framerate of 29.97 frames per second (FPS) and had been encoded into videos using AVC/H.264, HEVC/H.265, and AV1 codecs at resolutions from 8K to 480p. The images, video sequences, encoded videos, and various other statistics related to the media that make the dataset are stored online, published, and maintained on the repo on GitHub for non-commercial use. this proposed dataset is - as far as we know - the first to publish true 8K natural sequences; thus, it is important for the next level of applications dealing with multimedia such as video quality assessment, super-resolution, video coding, video compression, and many more. Paper: https://doi.org/10.1145/3587819.3592560

This dataset comprehends the 3D building information model (in IFC and Revit formats), manually elaborated based on the terrestrial laser scanner of the sequence 2 of ConSLAM, and the refined ground truth (GT) poses (in TUM format) of sessions 2, 3, 4, and 5 of the open-access ConSLAM dataset (which provides camera, LiDAR, and IMU measurements). This dataset enables the evaluation of pose estimation algorithms, such as localization, odometry, SLAM, and Bundle adjustment. Given the differences between the real-world and the 3D reference BIM model (Scan-Map deviations), the dataset is appropriate for evaluating long-term localization and mapping capabilities, i.e., methods that are robust to changes in the environment. In particular, in a real-world construction environment with normal clutter (not as in the Hilti dataset).

ThermoHands is the first benchmark dataset specifically designed for egocentric 3D hand pose estimation from thermal images. It addresses the challenges of hand pose estimation in low-light conditions and when the hand is occluded by gloves or other wearables—scenarios where traditional RGB or NIR-based systems struggle. The dataset contains approximately 96,000 synchronized multi-view, multi-spectral images collected from 28 participants under five varied scenarios involving changes in lighting, handwear, and background environments. Each sample includes thermal, NIR, RGB, and depth images, captured using a custom-built head-mounted sensor platform (HMSP) and a multi-view exocentric setup. Ground-truth 3D hand poses and shapes are automatically annotated using multi-view optimization based on the MANO model. Key features: Multi-spectral data: Egocentric and exocentric views of thermal, NIR, RGB, and depth modalities Rich annotations: 3D hand pose and shape ground truth via an automated optimization pipeline Realistic interactions: Both hand-object and hand-virtual interactions in diverse everyday scenarios Motivation and Use Cases: ThermoHands enables research into robust, privacy-preserving hand pose estimation for XR, AR/VR, and human-computer interaction applications. It is particularly useful for scenarios involving poor illumination, occlusion by gloves, or the need for passive sensing (e.g., avoiding NIR interference or power constraints). This dataset also introduces TherFormer, a novel transformer-based baseline tailored to thermal input, and benchmarks multiple SOTA methods under cross-spectral settings. Paper: https://arxiv.org/pdf/2403.09871 Project page: https://thermohands.github.io/ Code and models: https://github.com/LawrenceZ22/ThermoHands

~6 million synthetic depth frames for pose estimation from multiple cameras. Source: Real-Time Human Motion Capture with Multiple Depth Cameras

UW Indoor Scenes (UW-IS) Occluded dataset is curated using commodity hardware (Intel RealSense D435) to reflect real world robotics scenarios. It consists of two completely different indoor environments. The first environment is a lounge where the objects are placed on a tabletop. The second environment is a mock warehouse setup where the objects are placed on a shelf. For each of these environments, we have RGB-D images from 36 videos comprising five to seven objects each, taken from distances up to approximately 2m. The videos cover two different lighting conditions, three different levels of object separation for three different object categories (i.e., kitchen objects, food items, and tools/miscellaneous). The first level of object separation is such that there is no object occlusion. The second level of object separation is such that some occlusion occurs, while the third level is where the objects are placed extremely close together. Overall, the dataset considers 20 object classes and consists of 8,456 images, which have a total of 42,902 object instances. We also provide instance segmentation masks and 6D pose annotations for all the images generated using LabelFusion (Marion et al., 2018)

Data used for the paper SparsePoser: Real-time Full-body Motion Reconstruction from Sparse Data It contains over 1GB of high-quality motion capture data recorded with an Xsens Awinda system while using a variety of VR applications in Meta Quest devices.

Dataset consist of both real captures from Photoneo PhoXi structured light scanner devices annotated by hand and synthetic samples produced by custom generator. In comparison with existing datasets for 6D pose estimation, some notable differences include: * most of the captured bins are texture-less, made from uniform, single-colored materials, * all bins are of cuboid shape with different proportions. Compared to objects with complex geometry, bins consist of flat faces with edges, which are not guaranteed to be seen in the capture due to occlusion. Surface models of these bins are not provided, just their approximate bounding boxes, * PhoXi scanner provides high-resolution 3D geometry data, but no RGB data, with a rough and noisy gray-scale intensity image being the closest equivalent, * captures come from different devices with various intrinsic camera parameters. 3D point clouds contain these parameters implicitly as opposed to RGBD images. Due to its currently limited size, we recommend cross-validation instead of an explicit train-validation split. We plan to add more samples into the dataset. NOTE: Annotation files with suffixes _bad, _ish or _catastrophic should be ignored. It was not possible to annotate them correctly with our current toolset.

The dataset includes the synthetic data generated from rendering the 3D meshes of LM objects and several household objects in Blender for training 6D pose estimation algorithms. The whole dataset contains synthetic data for 18 objects (13 from LM and 5 from household objects), with 20,000 data samples for each object. Each data sample includes an RGB image in .png format and a depth image in .exr format. Each sample has the annotations of mask labels in .png format and the ground truth pose labels saved in .json files. Apart from the training data, the 3D meshes of the objects and the pre-trained models of the 6D pose estimation algorithm are also included. The whole dataset takes approximately ~1T of storage memory.

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Accidental Turntables contains a challenging set of 41,212 images of cars in cluttered backgrounds, motion blur and illumination changes that serves as a benchmark for 3D pose estimation. Source: Accidental Turntables: Learning 3D Pose by Watching Objects Turn Image Source: https://people.cs.umass.edu/~zezhoucheng/acci-turn/